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    Interpolation of the Mean Anomalies for Cloud Filling in Land Surface Temperature and Normalized Difference Vegetation Index

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    MilitinoIEEEps_5.pdf
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    Type
    Article
    Authors
    Militino, Ana F.
    Ugarte, M. Dolores
    Pérez-Goya, Unai
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics
    Statistics Program
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/655907
    
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    Abstract
    When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%.
    Citation
    Militino, A. F., Ugarte, M. D., Perez-Goya, U., & Genton, M. G. (2019). Interpolation of the Mean Anomalies for Cloud Filling in Land Surface Temperature and Normalized Difference Vegetation Index. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 6068–6078. doi:10.1109/tgrs.2019.2904193
    Sponsors
    This work was supported in part by project MTM2017-82553-R (AEI/FEDER, UE), in part by projects PI015-2016 and PI043-2017 (Government of Navarra, Spain), and in part by the Fundación CANObra Social Caixa-UNED Pamplona 2016 and 2017.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    10.1109/TGRS.2019.2904193
    Additional Links
    https://ieeexplore.ieee.org/document/8692724/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8692724
    ae974a485f413a2113503eed53cd6c53
    10.1109/TGRS.2019.2904193
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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